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Cloud Resource Demand Prediction to Achieve Efficient Resource Provisioning

Cloud Resource Demand Prediction to Achieve Efficient Resour...

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international conference of Signal processing and Intelligent Systems (ICSPIS)

作者： Sepideh Ebneyousef Saeed Ghazanfari-Rad Department of Electrical and Computer Engineering AleTaha University Tehran Iran

ISBN: (纸本)9781665476249

Cloud computing is a paradigm that uses techniques such as virtualization, time sharing and distributed computing to provide a wide range of hardware and software services for users based on a pay-as-you-go model, on-demand and via ubiquitous network access. Due to the scarcity of important cloud resources such as compute, network and storage, cloud service providers should carefully select, apply and distribute resources within the predefined boundaries of the cloud system meanwhile satisfy the requirements of running applications. One of the most critical components of the cloud computing architecture is resource allocation, where its effectiveness significantly affects the functionality of the entire cloud system. Implementing efficient resource allocation schemes and algorithms in cloud systems is not straightforward and faces several challenges including compromising between the required implementation costs and the utilization of resources, satisfying delay requirements of running applications, effective reallocation of resources, reducing computational complexity and scheduling workloads and tasks appropriately across a data center or in a heterogeneous multicloud environment. One of the goals of the consumers of cloud computing services is to complete tasks at minimal cost. In terms of cost efficiency, the goal of cloud providers is to maximize the resource utilization and thus the profits while cloud users are aimed at achieving the alternative goal which is to reduce the cost without compromising the quality of delivered services. There are various approaches of implementing resource allocation to compromise between maximizing the resource utilization and minimizing the consumer’s costs. In this article, we survey different scheduling algorithms and resource allocation approaches in the cloud computing environment, which include the prediction of resource requests in virtual machines and optimal allocation of resources to reduce costs and increase efficien

关键词： Cloud computing Costs Uncertainty Signal processing algorithms Prediction algorithms Virtual machining Resource management

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NetANNS: A High-Performance distributed Search Framework Based On In-Network Computing

NetANNS: A High-Performance Distributed Search Framework Bas...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Penghao Zhang Heng Pan Zhenyu Li Gaogang Xie Penglai Cui Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Purple Mountain Laboratories China Computer Network Information Center Chinese Academy of Sciences China

Approximate nearest neighbor search (ANNS) is the most basic and important algorithm in Database, Machine Learning and other applications. With the expansion of cloud computing, the academia focuses on the study of how to optimize distributed frameworks based on approximate nearest neighbor search such as MapReduce, and Memcached. We implement a new distributed ANNS search framework (NetANNS). The main contributions of NetANNS are to accelerate the data preprocessing with programmable switch, and integrate a variety of efficient ANNS algorithms so that it can choose the most suitable algorithm for each datasets. The experiments show that the search efficiency of NetANNS is about 2x than the common distributed ANNS frameworks which are implemented based on the framework of MapReduce.

关键词： Cloud computing Machine learning algorithms Data preprocessing Switches Nearest neighbor methods Approximation algorithms Database machines

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25th international European conference on parallel and distributed Computing, EuroPar 2019

25th International European Conference on Parallel and Distr...

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25th international European conference on parallel and distributed Computing, EuroPar 2019

ISBN: (纸本)9783030483395

The proceedings contain 63 papers. The special focus in this conference is on parallel and distributed Computing. The topics include: mF2C: The Evolution of Cloud Computing Towards an Open and Coordinated Ecosystem of Fogs and Clouds;academic Achievement Recognition and Verification Using Blockchain;crypto-Trading. Rechargeable Token-Based Smart Energy Market Enabled by Blockchain and IoT Technology;ethereum Transaction Performance Evaluation Using Test-Nets;blockchain-Based Trusted Cross-organizational Deliveries of Sensor-Equipped Parcels;towards a Trusted Support Platform for the Job Placement Task;blockchain Materialization as a General Purpose Technology: A Research Framework;on Refining Design Patterns for Smart Contracts;dataRaceOnAccelerator – A Micro-benchmark Suite for Evaluating Correctness Tools Targeting Accelerators;Adaptive Crown Scheduling for Streaming Tasks on Many-Core Systems with Discrete DVFS;application Topology Definition and Tasks Mapping for Efficient Use of Heterogeneous Resources;Toward Heterogeneous MPI+MPI Programming: Comparison of OpenMP and MPI Shared Memory Models;multicore Performance Prediction – Comparing Three Recent Approaches in a Case Study;exploiting Historical Data: Pruning Autotuning Spaces and Estimating the Number of Tuning Steps;advancing Automatic Code Generation for Agent-Based Simulations on Heterogeneous Hardware;Optimization of Data-parallel applications on Heterogeneous HPC Platforms for Dynamic Energy Through Workload Distribution;search-Based Scheduling for parallel Tasks on Heterogeneous Platforms;adaptation of Workflow Application Scheduling Algorithm to Serverless Infrastructure;CCAMP: OpenMP and OpenACC Interoperable Framework;HPC Requirements of High-Fidelity Flow Simulations for Aerodynamic applications;minimizing Self-adaptation Overhead in parallel Stream processing for Multi-cores;data-Adapted parallel Merge Sort;In Situ Visualization of Performance-Related Data in parallel CFD applications;time-Based C

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A parallel Drone Image Mosaic Method Based on Apache Spark 9th

A Parallel Drone Image Mosaic Method Based on Apache Spark

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9th EAI international conference on Cloud Computing, CloudComp 2019 and the 4th EAI international conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

作者： Wu, Yirui Ge, Lanbo Luo, Yuchi Teng, Deqiang Feng, Jun College of Computer and Information Hohai University Nanjing China

ISBN: (纸本)9783030485122

MapReduce has been widely used to process large-scale data in the past decade. Among the quantity of such cloud computing applications, we pay special attention to distributed mosaic methods based on numerous drone images, which suffers from costly processing time. In this paper, a novel computing framework called Apache Spark is introduced to pursue instant responses for the quantity of drone image mosaic requests. To assure high performance of Spark-based algorithms in a complex cloud computing environment, we specially design a distributed and parallel drone image mosaic method. By modifying to be fit for fast and parallel running, all steps of the proposed mosaic method can be executed in an efficient and parallel manner. We implement the proposed method on Apache Spark platform and apply it to a few self-collected datasets. Experiments indicate that our Spark-based parallel algorithm is of great efficiency and is robust to process low-quality drone aerial images. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Drones

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Deep neural networks based contactless fingerprint recognition

Deep neural networks based contactless fingerprint recogniti...

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international conference on New Technologies of Information and Communication (NTIC)

作者： Abderrahmane Herbadji Noubeil Guermat Zahid Akhtar Department of Electronics University of Setif-1 Setif Algeria Department of Electronics Faculty of Technology University of M’Sila M’Sila Algeria Department of Network and Computer Security State University of New York Polytechnic Institute Utica USA

For developing automatic and accurate system for human recognition, deep learning is now progressively becoming common in real-world biometrics applications. Fingerprint is one of the most important discriminative biometric characteristic due to its high reliability and uniqueness properties, which has led to a widespread use by law enforcement, forensic as well as in mobile devices user authentication. Contactless fingerprint recognition has achieved rapid development in recent years thanks to more hygienic and ubiquitous personal identification techniques. In this paper, we present deep neural networks (DNNs) based solutions for contactless fingerprint identification. More specifically, we show how existing DNNs can be deployed as a feature extractor for contactless fingerprint. Experimental analyses on publically available dataset with 336 subjects demonstrate the effectiveness of DNNs-based feature extractors. Moreover, experimental results illustrate best recognition performance in comparison with state-of-the-art texture descriptors.

关键词： Deep learning Measurement Law enforcement Neural networks Transfer learning Fingerprint recognition parallel processing

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INDENT: Incremental Online Decision Tree Training for Domain-Specific Systems-on-Chip

INDENT: Incremental Online Decision Tree Training for Domain...

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IEEE international conference on Computer-Aided Design

作者： Anish Krishnakumar Radu Marculescu Umit Ogras University of Wisconsin-Madison USA The University of Texas at Austin USA

The performance and energy efficiency potential of heterogeneous architectures has fueled domain-specific systems-on-chip (DSSoCs) that integrate general-purpose and domain-specialized hardware accelerators. Decision trees (DTs) perform high-quality, low-latency task scheduling to utilize the massive parallelism and heterogeneity in DSSoCs effectively. However, offline trained DT scheduling policies can quickly become ineffective when applications or hardware configurations change. There is a critical need for runtime techniques to train DTs incrementally without sacrificing accuracy since current training approaches have large memory and computational power requirements. To address this need, we propose INDENT, an incremental online DT framework to update the scheduling policy and adapt it to unseen scenarios. INDENT updates DT schedulers at runtime using only 1-8% of the original training data embedded during training. Thorough evaluations with hardware platforms and DSSoC simulators demonstrate that INDENT performs within 5% of a DT trained from scratch using the entire dataset and outperforms current state-of-the-art approaches.

关键词： Training Runtime Processor scheduling Memory management Training data parallel processing Decision trees

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An efficient hybrid MPI/OpenMP parallelization of the asynchronous ADMM algorithm

An efficient hybrid MPI/OpenMP parallelization of the asynch...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Qinnan Qiu Yongmei Lei Dongxia Wang Guozheng Wang School of Computer Engineering and Science Shanghai University Shanghai China

Alternating direction method of multipliers (ADMM) is an efficient algorithm to solve large-scale machine learning problems in a distributed environment. To make full use of the hierarchical memory model in modern high-performance computing systems, this paper implements a hybrid MPI/OpenMP parallelization of the asynchronous ADMM algorithm (AH-ADMM). The AH-ADMM algorithm updates local variables in parallel by OpenMP threads and exchanges information between MPI processes, which relieves memory and communication pressure by replacing multi-processing with multi-threading. Furthermore, for the SVM problem, the AH-ADMM algorithm speeds up the calculation of sub-problems through an efficient parallel optimization strategy. This paper effectively combines the features of both algorithm design and programming model. Experiments on the Ziqiang4000 high-performance cluster demonstrate that the AH-ADMM algorithm scales better and run faster than the existing distributed ADMM algorithms implemented by pure MPI. The AH-ADMM can reduce the communication overhead by up to 91.8% and increase the convergence rate by up to 36x. For large datasets, the AH-ADMM can scale well on the cluster which over 129 cores.

关键词： Support vector machines Machine learning algorithms parallel programming Multicore processing Scalability Clustering algorithms Machine learning

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Mapping Signal processing Algorithms into the Systolic Arrays 2

Mapping Signal Processing Algorithms into the Systolic Array...

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2nd international conference on Electrical, Communication and Computer Engineering, ICECCE 2020

作者： Snopce, Halil Aliu, Azir Luma, Artan Faculty of Contemporary Sciences and Technologies South East European University Tetovo Macedonia

ISBN: (数字)9781728171166

ISBN: (纸本)9781728171166

In this paper we present some techniques for construction of two dimensional mapping arrays, Appropriate for signal processing, called systolic arrays. These type of array algorithms are implemented on VLSI arrays. Taking into the consideration that matrix operations and applications are crucial for signal processing, we make an overview of the systolic processing for the case of matrix-matrix multiplication, QR decomposition and Singular value decomposition (SVD) of the matrix. Systolic arrays are very appropriate for parallel manipulations with matrices. Application domain of these arrays cover different areas especially the signal and image processing. Generally, all signal processing algorithms can be classified in two groups: Algorithms which perform the matrix operations and other special algorithms for signal processing. Most of the algorithms fall into the algorithms of matrix computations and other matrix transformations. Therefore, we consider that the techniques of construction of such arrays are very important in developing the new ideas and transformations which will enable to optimize and make them more efficient. © 2020 IEEE.

关键词： Systolic arrays

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Research of Thread Granularity Optimization Strategy for the Domestic DCU Accelerator

Research of Thread Granularity Optimization Strategy for the...

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Information Science, parallel and distributed Systems (ISPDS), international conference on

作者： Hengliang Guo Yuanyuan Yue Mengyu Bo Yaqu Liu Lin Han Aifang Su Henan Province Supercomputing Center Zhengzhou University Zhengzhou China School of Information Engineering Zhengzhou University Zhengzhou China Henan Province Supercomputing Center Zhengzhou University Zhengzhou China Henan Meteorological Service Zhengzhou China

ISBN: (纸本)9781665417396

In the field of high-performance parallel computing, GPUs are typically used to handle large-scale compute-intensive and data-intensive tasks. The domestic DCU accelerator is a new generation of GPU accelerators developed independently by China, which uses CPU plus DCU heterogeneous programming in line with the current mainstream model of high-performance calculation heterogeneous programming and is more suitable for the deployment of highly intensive calculation applications. Thread granularity controls the number of threads performing parallel tasks and is an important factor affecting the parallel performance of a program. To further improve the operational efficiency under the premise of ensuring the accuracy, this paper takes the classical matrix multiplication as an example, and uses the thread block optimization strategy around row or column span and proximity calculations to study the variation of runtime, GFLPOS and acceleration ratio of the kernel functions with different thread granularity for the same matrix scale. The experiments were conducted on the domestic DCU accelerator, and the results showed that for matrix multiplications of 512*512 scale, the acceleration ratios of 1.13179, 1.168495, and 1.23378 were achieved for row span calculation, row proximity calculation, and column proximity calculation, respectively, compared with the naive parallel program. While for matrix multiplication of 1024*1024 scale, the acceleration ratios of 1.973046, 1.833173, 1.770419, and 1.746696 were achieved for row span calculation, column span calculation, row proximity calculation and column proximity calculation, respectively, compared with the naive parallel program.

关键词： Information science Runtime Instruction sets Computational modeling Graphics processing units Programming parallel processing

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20th international conference on Algorithms and Architectures for parallel processing, ICA3PP 2020

20th International Conference on Algorithms and Architecture...

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20th international conference on Algorithms and Architectures for parallel processing, ICA3PP 2020

ISBN: (纸本)9783030602383

The proceedings contain 145 papers. The special focus in this conference is on Algorithms and Architectures for parallel processing. The topics include: An Improved Heterogeneous Dynamic List Schedule Algorithm;fastThetaJoin: An Optimization on Multi-way Data Stream θ -join with Range Constraints;a distributed Framework for Online Stream Data Clustering;end-System Aware Large File Transfer Solution for Rich Media applications over 5G Mobile Networks;broad Learning System with Proportional-Integral-Differential Gradient Descent;Accelerating De Novo Assembler WTDBG2 on Commodity Servers;Typing Everywhere with an EMG Keyboard: A Novel Myo Armband-Based HCI Tool;accelerating Pattern Matching on Intel Xeon Phi Processors;Redistributing and Optimizing High-Resolution Ocean Model POP2 to Million Sunway Cores;Efficient Sorting and Join on NVM-Based Hybrid Memory;performance Optimization for Feature Extraction Section of DeepChem;principal Component Analysis for Fingerprint Positioning;priority Based Service Placement Strategy in Heterogeneous Mobile Edge Computing;VTC: A Scheduling Framework Between Soft Real-Time and Hard Real-Time on Multimedia OS;A BSP Based Approach for NFAs Intersection;tight Bound of parallel Request Latency for Erasure-Coded distributed Storage System;High-Performance Simulations on GPUs Using Adaptive Time Steps;Performance Modeling of Stencil Computation on SW26010 Processors;Optimizing B+ -Tree Searches on Coupled CPU-GPU Architectures;OCVM: Optimizing the Isolation of Virtual Machines with Open-Channel SSDs;parallel SCC Detection Based on Reusing Warps and Coloring Partitions on GPUs;CANRT: A Client-Active NVM-Based Radix Tree for Fast Remote Access;distributed and parallel Ensemble Classification for Big Data Based on Kullback-Leibler Random Sample Partition;SWAF: A distributed Solar WSN Adaptive Framework.

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